1Overview & positioning
LogicMojo is a specialist AI training provider rather than a broad EdTech marketplace, and the whole program is built around one question: can a working Indian learner reach production-capable generative and agentic AI engineering in a single structured sequence, without taking a career break?
The combination is what earns the top position rather than any single feature. GenAI depth of the kind normally found only in specialist programs; ML and deep-learning grounding of the kind normally found only in premium university programs; and agentic currency — multi-framework agents, MCP, open-weight models, evaluation and LLMOps — that is genuinely rare at any price. All of it is delivered live in IST at a mid-band fee, with no bond and no income share agreement.
2GenAI & agentic curriculum breakdown
The progression runs across fifteen modules: Python and software engineering for LLM applications → ML and deep-learning essentials → transformers and attention → LLM fundamentals, tokens and context windows → prompt and context engineering with structured outputs and function calling → embeddings, vector databases and semantic search → production RAG with chunking strategy, hybrid retrieval, re-ranking and citations → RAG evaluation → fine-tuning and model adaptation → agents, planning, memory and tool use → agent frameworks, MCP and multi-agent orchestration → multi-modal applications → evaluation, guardrails and responsible AI → LLMOps, deployment and observability → GenAI system design and a learner-designed capstone.
Prerequisite support is built in rather than assumed: a Python and API onboarding track exists for switchers, and a free-tier plus local-inference path means the syllabus is completable without paid API credits.
Depth verdict
3Online delivery experience
- Live IST weekend batches (Sat–Sun, 9:00 AM – 12:00 PM), with the next batch starting in the coming month, taught by instructors with shipped LLM work rather than career trainers.
- In-session doubt resolution, mentor channels between sessions, and human code review on RAG and agent submissions — the single feature that most separates a paid program from the free stack.
- Recordings with a structured catch-up path, cohort accountability, and deferral options if work or life interrupts a batch.
- Continuous curriculum refresh against framework releases, rather than an annual academic revision cycle.
- A free-tier and local-inference path throughout, so API spend never becomes the reason a learner stalls at the fine-tuning or agents module.
4Projects & portfolio output
- Ten to fifteen progressive projects, escalating from a first LLM application to a learner-designed, deployed capstone.
- Evaluation and deployment are mandatory rather than optional: a project without an evaluation harness is not considered complete.
- Everything is documented for GitHub with architecture notes, and submissions receive human review — which is what converts a folder of notebooks into a portfolio that survives an interview.
5Certification & credential
LogicMojo course completion certificate; there is no university affiliation. That is stated plainly here because the honest position is that the portfolio, not the certificate, is the credential that moves a GenAI hiring decision.
My field notes — what I found when I evaluated this myself
When I built my reference agentic assistant against this syllabus, it was the only one in the list that never left me searching elsewhere for a missing piece — evaluation, guardrails and deployment were already there, in sequence. The part that changed my opinion was the code review: the feedback I saw was about retrieval strategy and failure handling, not about whether the notebook ran. What I would tell a friend, unchanged: the reason it works is also the reason it is hard. You are doing ML and deep learning before you touch LLMs, and if you want a six-week GenAI sprint you will resent week three.
Beginner readiness
9.4 / 10 — the most complete zero-to-job ramp in this list
Built for two audiences that most GenAI programs quietly exclude: the complete beginner who has never written a for-loop, and the working professional with eight years of non-AI engineering who cannot take a career break. Both start in the same foundation track and converge on the same agentic capstone.
Prerequisites: No prior AI, ML or data-science exposure required. Graduate-level maths is not assumed — linear algebra and probability are re-taught only to the depth an LLM engineer actually uses. Basic computer literacy and roughly 12–15 hours per week are the real prerequisites.
Foundational ramp-up
- Pre-course Python and API onboarding track for non-coders: syntax, data structures, functions, virtual environments, Git, REST calls and JSON handling before any model code is touched.
- Maths-for-AI primer taught as intuition plus code, not proofs — vectors, dot products, cosine similarity, gradients and probability distributions demonstrated on the embeddings you later use in RAG.
- Every module ends with a graded checkpoint; learners who miss it are re-taught in a doubt-clearing slot rather than pushed forward with a gap.
- Batch-repeat access: a learner who falls behind can re-attend the same module in the next live batch.
Learning support structure
- Live IST weekend classes (Sat–Sun, 9:00 AM – 12:00 PM) — questions are answered in the room, not filed as tickets.
- Dedicated doubt-clearing sessions scheduled separately from lectures, so lecture time is not consumed by debugging.
- Human code review on submitted projects — reviewers read your retrieval strategy and agent control flow, not just whether the notebook runs.
- Peer cohort groups and a persistent learner community for pair-debugging, plus teaching-assistant support between sessions.
- Lifetime access to recordings and updated material as frameworks change.
Mentorship: One-to-one mentorship with instructors who ship LLM systems rather than career trainers — used for project scoping, architecture review before you over-engineer, and interview debriefs after each attempt.
Projects, capstone & industry-level work
- 10–15 progressive projects, each one a layer of the production stack rather than a standalone demo.
- Beginner tier: prompt-engineered structured-output application, function-calling assistant, embeddings-based semantic search over your own documents.
- Intermediate tier: production RAG over a messy real-world corpus with chunking strategy, hybrid retrieval, re-ranking, citations and a RAG evaluation harness that reports faithfulness and retrieval hit-rate.
- Advanced tier: LoRA/QLoRA fine-tune of an open-weight model with a before-and-after eval; a multi-agent workflow with planning, memory and tool use built twice on different frameworks; an MCP server exposing your own tools to an agent.
- Capstone: a learner-designed GenAI or agentic system deployed behind FastAPI in Docker with guardrails, tracing, cost monitoring and a written system-design defence — the artefact you take into interviews.
Beginner-to-deployment curriculum ladder
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Placement & job assistance
- Placement-first structure: career work is embedded in the timeline from the mid-point rather than bolted on after the capstone.
- Structured job-assistance pipeline — profile audit, resume rebuild around shipped GenAI artefacts, LinkedIn and GitHub optimisation, mock interviews, then referral and application support.
- Mock interview rounds cover the three real GenAI loops: Python and DSA screening, LLM/RAG system design, and an agentic architecture discussion with evaluation and guardrail follow-ups.
- Resume and LinkedIn workshops rewrite the profile around measurable artefacts — retrieval hit-rate improved, eval score moved, cost per query reduced — rather than course names.
- One-to-one career counselling on realistic target roles by background: fresher versus switcher versus senior engineer, and product company versus GCC versus services.
- Published learner outcomes are collected on the provider's success-story page — read them directly rather than trusting any summary: logicmojo.com/success-story. The full syllabus and batch schedule are on the GenAI & Agentic AI course page.
- Honest limitation: this is job assistance, not a placement guarantee. There is no bond and no income-share agreement, which also means no contractual promise of a job. Treat any figure quoted elsewhere as unverified unless the provider publishes the methodology.
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Learner outcome feedback (illustrative profiles)
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6Who this is genuinely for
- Software engineers with 2–8 years of experience moving into GenAI, with 10–15 hours a week to give.
- Data scientists and ML engineers adding RAG, fine-tuning, agents, evaluation and LLMOps to an existing modelling background.
- Career switchers who need prerequisite support but refuse a prompt-only overview sold as engineering training.
- Self-taught prompt tinkerers with half-working notebooks who need a spine, code review and a coherent portfolio.
- Professionals who want agents, MCP and evaluation genuinely taught rather than demonstrated once.
7Who should avoid it
- You need a university credential above everything else.
- Your budget is under ₹20,000.
- You cannot attend live sessions in IST with any regularity.
- You want GenAI literacy for meetings rather than engineering capability.
- You are an experienced ML engineer who only needs the GenAI layer and does not want to sit through foundations.
- You are on a research pathway rather than an applied engineering one.
8Fees, EMI, API costs & value
- ₹87,000 (GST inclusive) for the 7-month program, with EMI available and no bond or income share agreement.
- Ask whether API and compute credits are included. In practice the free-tier and Colab path means realistic self-funded running costs stay at the low end of the ₹3,000–₹15,000 band that any serious GenAI program implies.
- Framed as capability per rupee, it is the strongest ratio among the paid programs here — with the honest qualification that a disciplined self-learner can approach the same knowledge for close to ₹0 through Hugging Face and DeepLearning.AI, minus the review and accountability.
9Career support & outcomes
- Career guidance, portfolio review, and GenAI-role interview preparation built around RAG architecture and agent design cases rather than generic aptitude prep.
- Project defence practice — being asked why you chose that chunk size, that retrieval strategy, that model — which mirrors how Indian GenAI interviews actually run.
- What it is not: a guaranteed-placement program. There is no placement guarantee, no partner-company quota implied here, and nothing in this article should be read as one.
10aPros
- The only syllabus here that treats RAG evaluation as a graded topic rather than a passing mention.
- Multi-framework agent coverage (LangGraph, CrewAI, AutoGen, Agents SDK) plus MCP, so you learn patterns rather than one vendor's syntax.
- Open-weight models and local inference are taught as first-class, which matches Indian data-residency constraints in BFSI, health and government work.
- Human code review on agent and RAG submissions — rare below ₹2L.
- LLMOps and deployment are part of the sequence, not an epilogue.
- Live IST scheduling with recordings and deferral options for shift and on-call workers.
- No bond, no ISA, and a mid-band fee against premium-priced competitors.
- Capstone is learner-designed, which produces portfolio variety instead of thirty identical chatbots.
10bCons
- Not a GenAI-only sprint — the ML/DL foundation adds weeks that experienced practitioners will find redundant.
- No university or global-brand credential to put in front of an HR filter.
- Smaller brand recognition than Udacity, DataCamp or Great Learning.
- Fixed live schedule punishes learners with unpredictable work hours.
- Placement infrastructure is genuinely smaller than the large placement-first bootcamps'.
- Fee sits above every free and marketplace alternative, which matters if you are self-funding on a student budget.
- Requires 10–15 hours a week; below that, the agents and LLMOps modules will outrun you.
- Framework churn is a risk here as everywhere — no provider can guarantee today's stack is next year's.
11Verdict, rating & next step
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